Confused by Analytics? Demystifying the Basics

There is confusion in the market about the terms associated with this new and emerging technology. You’ve probably heard the phrase “reporting and analytics” tossed around. These terms are often used interchangeably, however they perform two very different functions. The same is true for the terms data, information, insights, and dashboards.

Data – Data is the information that you collect from students, operations, learning systems, social media, and other sources. Data is the raw unprocessed facts, and it is very difficult to make sense of individual data points.

Information – Information is data that is processed, organized, structured, or presented in a given context to make it useful.

Insights – Insights are the value extracted from the data and information. It is what you have learned from the data and the conclusion once have reviewed and analyzed the information.

Analytics – Analytics is the process of exploring data and reports in order to extract meaningful insights, which is used to better understand and improve business performance. (Source: Adobe)

Reporting – Reporting is the process of organizing data into informational summaries in order to monitor how different areas of a business are performing. (Source: Adobe)

Dashboards – A dashboard is a set of data visualization tools that aggregate and displays information at a high level. It summarizes key performance indicators (KPIs) and important data at-a-glance.

Once data has been processed and formatted for analysis, there are four types of analytics that each offer a different insight; descriptive, diagnostic, predictive, and prescriptive. The simplest is descriptive analytics and the most complex is prescriptive analytics.

Two of the categories look at the past while the other two categories look at the future.

Let’s take a closer look.

Descriptive Analytics– Descriptive analytics look at the past and answer the question what happened. It looks at historical data points and lets you know that something is either working or not working. For example, it could tell you that you are losing your higher achieving students after their freshman year at a greater rate that the students you assumed to be at risk.

Diagnostic Analytics– Diagnostic analytics also looks at the past and helps you answer the question "why did it happen?" by enabling you to drill down into information to determine the root cause of a problem. Building on the previous example, you may learn that many of these students transferred to programs at competitive institutions because they never felt your institution was their first choice or best fit, and were not academically engaged.

Predictive Analytics – Predictive analytics look at the future and determines what will likely happen. It looks at the information derived from descriptive and diagnostic analytics and predicts trend and forecasts. For example, you may find a particular combination of student entry characteristics, scholarship packages, and course of study result in an increased chance of departure. This model helps institutions identify students at risk of departure so they can undertake additional efforts to retain those students.

Prescriptive Analytics – Prescriptive analytics is very complex and still in its infancy. It looks at the future and recommends what action to take to rectify a future problem or take full advantage of a promising trend. In the previous example, this could mean recommending increased gift aid, additional mentorship programs, or an enhanced honors program to keep students engaged.

Most institutions have some form of descriptive and/or diagnostic level analytics at least on a departmental level. Predictive analytics is the next step in the journey and it can be powerful and transformative. It begins by asking a big question and requires a level of analytics maturity within the organization to derive true value.